Review and Proposal for a Classification System of Soft Robots Inspired by Animal Morphology

López-González, A.; Tejada, Juan C.; Lopez-Romero, J.C.

doi:10.3390/biomimetics8020192

Cited by 7 publications

(1 citation statement)

References 161 publications

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“…The time series is input to the perceptron, and the entropy value is calculated at the output. Such sensor can be applied not only in the field of computational neuroscience, but also to create humanoid robots [ 34 ] and animal robots [ 35 , 36 ], including devices with limited resources [ 37 ], as well as to improve neuroadaptive learning algorithms for robust control in constrained nonlinear systems [ 38 , 39 ]. For example, in biosimilar models where information is distributed as a spike train, changes in the packet frequency are often studied [ 40 , 41 ].…”

Section: Introductionmentioning

confidence: 99%

A Bio-Inspired Chaos Sensor Model Based on the Perceptron Neural Network: Machine Learning Concept and Application for Computational Neuro-Science

Velichko

Борисков

Belyaev

et al. 2023

Sensors

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The study presents a bio-inspired chaos sensor model based on the perceptron neural network for the estimation of entropy of spike train in neurodynamic systems. After training, the sensor on perceptron, having 50 neurons in the hidden layer and 1 neuron at the output, approximates the fuzzy entropy of a short time series with high accuracy, with a determination coefficient of R2~0.9. The Hindmarsh–Rose spike model was used to generate time series of spike intervals, and datasets for training and testing the perceptron. The selection of the hyperparameters of the perceptron model and the estimation of the sensor accuracy were performed using the K-block cross-validation method. Even for a hidden layer with one neuron, the model approximates the fuzzy entropy with good results and the metric R2~0.5 ÷ 0.8. In a simplified model with one neuron and equal weights in the first layer, the principle of approximation is based on the linear transformation of the average value of the time series into the entropy value. An example of using the chaos sensor on spike train of action potential recordings from the L5 dorsal rootlet of rat is provided. The bio-inspired chaos sensor model based on an ensemble of neurons is able to dynamically track the chaotic behavior of a spike signal and transmit this information to other parts of the neurodynamic model for further processing. The study will be useful for specialists in the field of computational neuroscience, and also to create humanoid and animal robots, and bio-robots with limited resources.

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